A SLAM algorithm based on an iterated central difference particle filter
Yingjie Qi · Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University · 2012
In order to improve the accuracy of position estimation in the simultaneous localization and mapping(SLAM) algorithm,this paper provided an iterated central difference Kalman filter(ICDKF) to compute the proposed distribution in the widely used Rao-blackwellized particle filter(RBPF) instead of the extended Kalman filter(EKF).Furthermore,by combining new observation data,the proposed distribution was moved closer to the posteriori probability,and the position of the intelligent vehicle was accurately estimated,therefore updating the position of the feature map by the ICDKF.This algorithm decreased computational complexity,and improved the estimation performance of the system along with the stability of the iterated algorithm without decreasing accuracy.Simulation results validate the effectiveness of the proposed algorithm.